Misinterpretations of the ‘p value’: a brief primer for academic sports medicine
Bibliographic record
Abstract
When comparing treatment groups, the p value is a statistical measure that summarises the chance (‘p’ for probability) that one would obtain the observed result (or more extreme), if and only if, the treatment is ineffective (ie, under the assumption of the ‘null’ hypothesis). The p value does not tell us the probability that the null hypothesis is true.1 This editorial discusses how some common misinterpretations of the p value may impact sports medicine research. Although presented from a treatment standpoint, the same principles hold for causes or prevention. p Values are probabilities, yet often interpreted based on a categorical cut-off, generally at the level of 0.05 (ie, 5%). Anything below is considered a ‘statistically significant difference’ and vice versa. However, one would not change a decision to buy a lottery ticket if the chance of winning was 4.9% (p=0.049i) instead of 5.1% (p=0.051). Consider a study where 100 participants who were given an injury prevention programme had six injuries, and 100 participants …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.089 | 0.315 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.020 | 0.041 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".